Aggregating Judgements: Logical and Probabilistic Approaches

Course Information
  • Lecturer: Eric Pacuit (website)
  • Meeting Times: 17:00 - 18:30
  • Date: August 5 - 10, 2019
  • Location: Riga, Latvia
  • This course will introduce the key results (including proofs) and the main research themes in the study of judgement aggregation and the wisdom of the crowds. The course will focus on both logical and probabilistic models of judgement aggregation. The primary objective is to introduce the main mathematical methods and conceptual ideas found in this literature. In addition, I will state and prove the central results found in this literature. To facilitate understanding the main proof techniques and results, I will spend much of the time working through the theorems at the blackboard.

    Of course, there is a large literature concerning each of the theorems and topics listed above. My goal will not to be to survey this entire literature. Instead, I present enough background material to state and prove each of the theorems mentioned above. This will provide students with a solid foundation to engage with this fascinating research area.

Day 1    Introduction to judgement aggregation
Day 2    Impossibility theorems and characterization results
  • Slides

  • U. Endriss, Judgment Aggregation. In F. Brandt, V. Conitzer, U. Endriss, J. Lang, and A. D. Procaccia, editors, Handbook of Computational Social Choice, Cambridge University Press, 2016. (For a somewhat different perspective than what I presented during the lecture)

Day 3    Probabilistic opinion pooling
Day 4    Distance-based aggregation; opinion dynamics; Aumann's agreeing to disagree theorem
Day 5    Opinion merge; belief polarization; concluding remarks